Overview

Dataset statistics

Number of variables29
Number of observations786363
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory158.2 MiB
Average record size in memory211.0 B

Variable types

Numeric9
Categorical17
Boolean3

Warnings

echoBuffer has constant value "" Constant
merchantCity has constant value "" Constant
merchantState has constant value "" Constant
merchantZip has constant value "" Constant
posOnPremises has constant value "" Constant
recurringAuthInd has constant value "" Constant
transactionDateTime has a high cardinality: 776637 distinct values High cardinality
merchantName has a high cardinality: 2490 distinct values High cardinality
currentExpDate has a high cardinality: 165 distinct values High cardinality
accountOpenDate has a high cardinality: 1820 distinct values High cardinality
dateOfLastAddressChange has a high cardinality: 2184 distinct values High cardinality
accountNumber is highly correlated with customerIdHigh correlation
customerId is highly correlated with accountNumberHigh correlation
cardCVV is highly correlated with enteredCVVHigh correlation
enteredCVV is highly correlated with cardCVVHigh correlation
transactionDateTime is uniformly distributed Uniform
transactionAmount has 22225 (2.8%) zeros Zeros
currentBalance has 33678 (4.3%) zeros Zeros

Reproduction

Analysis started2021-03-24 00:30:05.825878
Analysis finished2021-03-24 00:33:53.413125
Duration3 minutes and 47.59 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

accountNumber
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5000
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean537232599.5
Minimum100088067
Maximum999389635
Zeros0
Zeros (%)0.0%
Memory size6.0 MiB
2021-03-23T20:33:53.505347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum100088067
5-th percentile151258718
Q1330133277
median507456073
Q3767620004
95-th percentile948004238
Maximum999389635
Range899301568
Interquartile range (IQR)437486727

Descriptive statistics

Standard deviation255421092.3
Coefficient of variation (CV)0.4754385578
Kurtosis-1.21865435
Mean537232599.5
Median Absolute Deviation (MAD)218237767
Skewness0.1124155098
Sum4.224598386 × 1014
Variance6.52399344 × 1016
MonotocityNot monotonic
2021-03-23T20:33:53.637116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38068024132850
 
4.2%
88281513413189
 
1.7%
57088486310867
 
1.4%
24625125310172
 
1.3%
3693080357229
 
0.9%
7245189776283
 
0.8%
8949388336101
 
0.8%
4197095145930
 
0.8%
8328422015850
 
0.7%
2083196535235
 
0.7%
Other values (4990)682657
86.8%
ValueCountFrequency (%)
10008806775
< 0.1%
10010875218
 
< 0.1%
100328049103
< 0.1%
10066362631
 
< 0.1%
100737756178
< 0.1%
ValueCountFrequency (%)
9993896359
 
< 0.1%
999283629141
 
< 0.1%
99925870488
 
< 0.1%
999257059160
 
< 0.1%
999086814677
0.1%

customerId
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5000
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean537232599.5
Minimum100088067
Maximum999389635
Zeros0
Zeros (%)0.0%
Memory size6.0 MiB
2021-03-23T20:33:53.772432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum100088067
5-th percentile151258718
Q1330133277
median507456073
Q3767620004
95-th percentile948004238
Maximum999389635
Range899301568
Interquartile range (IQR)437486727

Descriptive statistics

Standard deviation255421092.3
Coefficient of variation (CV)0.4754385578
Kurtosis-1.21865435
Mean537232599.5
Median Absolute Deviation (MAD)218237767
Skewness0.1124155098
Sum4.224598386 × 1014
Variance6.52399344 × 1016
MonotocityNot monotonic
2021-03-23T20:33:53.898823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38068024132850
 
4.2%
88281513413189
 
1.7%
57088486310867
 
1.4%
24625125310172
 
1.3%
3693080357229
 
0.9%
7245189776283
 
0.8%
8949388336101
 
0.8%
4197095145930
 
0.8%
8328422015850
 
0.7%
2083196535235
 
0.7%
Other values (4990)682657
86.8%
ValueCountFrequency (%)
10008806775
< 0.1%
10010875218
 
< 0.1%
100328049103
< 0.1%
10066362631
 
< 0.1%
100737756178
< 0.1%
ValueCountFrequency (%)
9993896359
 
< 0.1%
999283629141
 
< 0.1%
99925870488
 
< 0.1%
999257059160
 
< 0.1%
999086814677
0.1%

creditLimit
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10759.46446
Minimum250
Maximum50000
Zeros0
Zeros (%)0.0%
Memory size6.0 MiB
2021-03-23T20:33:54.004797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile500
Q15000
median7500
Q315000
95-th percentile50000
Maximum50000
Range49750
Interquartile range (IQR)10000

Descriptive statistics

Standard deviation11636.17489
Coefficient of variation (CV)1.08148272
Kurtosis5.173764693
Mean10759.46446
Median Absolute Deviation (MAD)5000
Skewness2.280312163
Sum8460844750
Variance135400566.1
MonotocityNot monotonic
2021-03-23T20:33:54.102525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5000201863
25.7%
15000139307
17.7%
750097913
12.5%
250075429
 
9.6%
2000068629
 
8.7%
1000056889
 
7.2%
5000048781
 
6.2%
100036430
 
4.6%
25034025
 
4.3%
50027097
 
3.4%
ValueCountFrequency (%)
25034025
 
4.3%
50027097
 
3.4%
100036430
 
4.6%
250075429
 
9.6%
5000201863
25.7%
ValueCountFrequency (%)
5000048781
 
6.2%
2000068629
8.7%
15000139307
17.7%
1000056889
7.2%
750097913
12.5%

availableMoney
Real number (ℝ)

Distinct521915
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6250.725369
Minimum-1005.63
Maximum50000
Zeros0
Zeros (%)0.0%
Memory size6.0 MiB
2021-03-23T20:33:54.431815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1005.63
5-th percentile178.67
Q11077.42
median3184.86
Q37500
95-th percentile19911.672
Maximum50000
Range51005.63
Interquartile range (IQR)6422.58

Descriptive statistics

Standard deviation8880.783989
Coefficient of variation (CV)1.420760546
Kurtosis10.27296495
Mean6250.725369
Median Absolute Deviation (MAD)2524.42
Skewness2.999323595
Sum4915339154
Variance78868324.26
MonotocityNot monotonic
2021-03-23T20:33:54.598043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2506015
 
0.8%
50005400
 
0.7%
150004254
 
0.5%
75004069
 
0.5%
5002811
 
0.4%
25002809
 
0.4%
100002757
 
0.4%
200002496
 
0.3%
10001865
 
0.2%
500001207
 
0.2%
Other values (521905)752680
95.7%
ValueCountFrequency (%)
-1005.631
< 0.1%
-972.121
< 0.1%
-936.081
< 0.1%
-930.551
< 0.1%
-916.031
< 0.1%
ValueCountFrequency (%)
500001207
0.2%
49999.721
 
< 0.1%
49999.621
 
< 0.1%
49999.431
 
< 0.1%
49998.891
 
< 0.1%

transactionDateTime
Categorical

HIGH CARDINALITY
UNIFORM

Distinct776637
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2016-12-25T14:04:15
 
4
2016-05-28T14:24:41
 
4
2016-08-26T03:09:48
 
3
2016-05-11T01:01:40
 
3
2016-04-14T13:11:10
 
3
Other values (776632)
786346 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters14940897
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique766989 ?
Unique (%)97.5%

Sample

1st row2016-08-13T14:27:32
2nd row2016-10-11T05:05:54
3rd row2016-11-08T09:18:39
4th row2016-12-10T02:14:50
5th row2016-03-24T21:04:46
ValueCountFrequency (%)
2016-12-25T14:04:154
 
< 0.1%
2016-05-28T14:24:414
 
< 0.1%
2016-08-26T03:09:483
 
< 0.1%
2016-05-11T01:01:403
 
< 0.1%
2016-04-14T13:11:103
 
< 0.1%
2016-04-06T18:26:243
 
< 0.1%
2016-09-27T18:00:403
 
< 0.1%
2016-01-26T08:54:303
 
< 0.1%
2016-06-20T20:39:363
 
< 0.1%
2016-04-23T12:11:333
 
< 0.1%
Other values (776627)786331
> 99.9%
2021-03-23T20:34:01.015167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2016-05-28t14:24:414
 
< 0.1%
2016-12-25t14:04:154
 
< 0.1%
2016-09-14t23:41:593
 
< 0.1%
2016-12-23t12:09:173
 
< 0.1%
2016-05-30t07:54:143
 
< 0.1%
2016-09-27t07:48:423
 
< 0.1%
2016-10-12t00:25:483
 
< 0.1%
2016-11-19t12:58:003
 
< 0.1%
2016-08-13t13:09:463
 
< 0.1%
2016-11-25t11:39:513
 
< 0.1%
Other values (776627)786331
> 99.9%

Most occurring characters

ValueCountFrequency (%)
02588303
17.3%
12316631
15.5%
21900367
12.7%
-1572726
10.5%
:1572726
10.5%
61150941
7.7%
T786363
 
5.3%
3695491
 
4.7%
5628805
 
4.2%
4623835
 
4.2%
Other values (3)1104709
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11009082
73.7%
Dash Punctuation1572726
 
10.5%
Other Punctuation1572726
 
10.5%
Uppercase Letter786363
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
02588303
23.5%
12316631
21.0%
21900367
17.3%
61150941
10.5%
3695491
 
6.3%
5628805
 
5.7%
4623835
 
5.7%
8369100
 
3.4%
7368189
 
3.3%
9367420
 
3.3%
ValueCountFrequency (%)
-1572726
100.0%
ValueCountFrequency (%)
T786363
100.0%
ValueCountFrequency (%)
:1572726
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common14154534
94.7%
Latin786363
 
5.3%

Most frequent character per script

ValueCountFrequency (%)
02588303
18.3%
12316631
16.4%
21900367
13.4%
-1572726
11.1%
:1572726
11.1%
61150941
8.1%
3695491
 
4.9%
5628805
 
4.4%
4623835
 
4.4%
8369100
 
2.6%
Other values (2)735609
 
5.2%
ValueCountFrequency (%)
T786363
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII14940897
100.0%

Most frequent character per block

ValueCountFrequency (%)
02588303
17.3%
12316631
15.5%
21900367
12.7%
-1572726
10.5%
:1572726
10.5%
61150941
7.7%
T786363
 
5.3%
3695491
 
4.7%
5628805
 
4.2%
4623835
 
4.2%
Other values (3)1104709
7.4%

transactionAmount
Real number (ℝ≥0)

ZEROS

Distinct66038
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.985791
Minimum0
Maximum2011.54
Zeros22225
Zeros (%)2.8%
Memory size6.0 MiB
2021-03-23T20:34:01.285751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.44
Q133.65
median87.9
Q3191.48
95-th percentile433.35
Maximum2011.54
Range2011.54
Interquartile range (IQR)157.83

Descriptive statistics

Standard deviation147.725569
Coefficient of variation (CV)1.078400672
Kurtosis6.446378027
Mean136.985791
Median Absolute Deviation (MAD)66.44
Skewness2.092246265
Sum107720557.5
Variance21822.84374
MonotocityNot monotonic
2021-03-23T20:34:01.418687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
022225
 
2.8%
8.21132
 
< 0.1%
49.3125
 
< 0.1%
8.81124
 
< 0.1%
4.39120
 
< 0.1%
7.53119
 
< 0.1%
8.22117
 
< 0.1%
4.69115
 
< 0.1%
8.04112
 
< 0.1%
36.01111
 
< 0.1%
Other values (66028)763063
97.0%
ValueCountFrequency (%)
022225
2.8%
0.0160
 
< 0.1%
0.0256
 
< 0.1%
0.0332
 
< 0.1%
0.0456
 
< 0.1%
ValueCountFrequency (%)
2011.541
< 0.1%
1905.31
< 0.1%
1873.971
< 0.1%
1780.61
< 0.1%
1772.631
< 0.1%

merchantName
Categorical

HIGH CARDINALITY

Distinct2490
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
Uber
 
25613
Lyft
 
25523
oldnavy.com
 
16992
staples.com
 
16980
alibaba.com
 
16959
Other values (2485)
684296 

Length

Max length30
Median length13
Mean length14.13677526
Min length4

Characters and Unicode

Total characters11116637
Distinct characters64
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowUber
2nd rowAMC #191138
3rd rowPlay Store
4th rowPlay Store
5th rowTim Hortons #947751
ValueCountFrequency (%)
Uber25613
 
3.3%
Lyft25523
 
3.2%
oldnavy.com16992
 
2.2%
staples.com16980
 
2.2%
alibaba.com16959
 
2.2%
apple.com16898
 
2.1%
walmart.com16873
 
2.1%
cheapfast.com16858
 
2.1%
ebay.com16842
 
2.1%
target.com16813
 
2.1%
Other values (2480)600012
76.3%
2021-03-23T20:34:01.697566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
putt61424
 
3.8%
amc37942
 
2.3%
ecards33072
 
2.0%
ez30922
 
1.9%
services27996
 
1.7%
fresh26224
 
1.6%
uber25613
 
1.6%
lyft25523
 
1.6%
online24832
 
1.5%
gas23910
 
1.5%
Other values (2541)1318851
80.6%

Most occurring characters

ValueCountFrequency (%)
849946
 
7.6%
e718185
 
6.5%
a653772
 
5.9%
s537605
 
4.8%
o535320
 
4.8%
t516495
 
4.6%
n462311
 
4.2%
i377087
 
3.4%
r343496
 
3.1%
c338300
 
3.0%
Other values (54)5784120
52.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6435319
57.9%
Decimal Number1973023
 
17.7%
Uppercase Letter1256921
 
11.3%
Space Separator849946
 
7.6%
Other Punctuation588811
 
5.3%
Dash Punctuation12617
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
S125613
 
10.0%
C116700
 
9.3%
P109916
 
8.7%
A93360
 
7.4%
M90446
 
7.2%
B86290
 
6.9%
F72510
 
5.8%
D72228
 
5.7%
R50488
 
4.0%
H47930
 
3.8%
Other values (15)391440
31.1%
ValueCountFrequency (%)
e718185
11.2%
a653772
 
10.2%
s537605
 
8.4%
o535320
 
8.3%
t516495
 
8.0%
n462311
 
7.2%
i377087
 
5.9%
r343496
 
5.3%
c338300
 
5.3%
l305047
 
4.7%
Other values (14)1647701
25.6%
ValueCountFrequency (%)
1223145
11.3%
4214795
10.9%
2213836
10.8%
3204038
10.3%
9202519
10.3%
6200498
10.2%
8193623
9.8%
5190057
9.6%
7173770
8.8%
0156742
7.9%
ValueCountFrequency (%)
#332018
56.4%
.213742
36.3%
'43051
 
7.3%
ValueCountFrequency (%)
849946
100.0%
ValueCountFrequency (%)
-12617
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7692240
69.2%
Common3424397
30.8%

Most frequent character per script

ValueCountFrequency (%)
e718185
 
9.3%
a653772
 
8.5%
s537605
 
7.0%
o535320
 
7.0%
t516495
 
6.7%
n462311
 
6.0%
i377087
 
4.9%
r343496
 
4.5%
c338300
 
4.4%
l305047
 
4.0%
Other values (39)2904622
37.8%
ValueCountFrequency (%)
849946
24.8%
#332018
 
9.7%
1223145
 
6.5%
4214795
 
6.3%
2213836
 
6.2%
.213742
 
6.2%
3204038
 
6.0%
9202519
 
5.9%
6200498
 
5.9%
8193623
 
5.7%
Other values (5)576237
16.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII11116637
100.0%

Most frequent character per block

ValueCountFrequency (%)
849946
 
7.6%
e718185
 
6.5%
a653772
 
5.9%
s537605
 
4.8%
o535320
 
4.8%
t516495
 
4.6%
n462311
 
4.2%
i377087
 
3.4%
r343496
 
3.1%
c338300
 
3.0%
Other values (54)5784120
52.0%

acqCountry
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
US
774709 
 
4562
MEX
 
3130
CAN
 
2424
PR
 
1538

Length

Max length3
Median length2
Mean length1.995460112
Min length0

Characters and Unicode

Total characters1569156
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS
ValueCountFrequency (%)
US774709
98.5%
4562
 
0.6%
MEX3130
 
0.4%
CAN2424
 
0.3%
PR1538
 
0.2%
2021-03-23T20:34:01.939910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T20:34:02.018406image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
us774709
99.1%
mex3130
 
0.4%
can2424
 
0.3%
pr1538
 
0.2%

Most occurring characters

ValueCountFrequency (%)
U774709
49.4%
S774709
49.4%
M3130
 
0.2%
E3130
 
0.2%
X3130
 
0.2%
C2424
 
0.2%
A2424
 
0.2%
N2424
 
0.2%
P1538
 
0.1%
R1538
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1569156
100.0%

Most frequent character per category

ValueCountFrequency (%)
U774709
49.4%
S774709
49.4%
M3130
 
0.2%
E3130
 
0.2%
X3130
 
0.2%
C2424
 
0.2%
A2424
 
0.2%
N2424
 
0.2%
P1538
 
0.1%
R1538
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1569156
100.0%

Most frequent character per script

ValueCountFrequency (%)
U774709
49.4%
S774709
49.4%
M3130
 
0.2%
E3130
 
0.2%
X3130
 
0.2%
C2424
 
0.2%
A2424
 
0.2%
N2424
 
0.2%
P1538
 
0.1%
R1538
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1569156
100.0%

Most frequent character per block

ValueCountFrequency (%)
U774709
49.4%
S774709
49.4%
M3130
 
0.2%
E3130
 
0.2%
X3130
 
0.2%
C2424
 
0.2%
A2424
 
0.2%
N2424
 
0.2%
P1538
 
0.1%
R1538
 
0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
US
778511 
MEX
 
3143
CAN
 
2426
PR
 
1559
 
724

Length

Max length3
Median length2
Mean length2.005240582
Min length0

Characters and Unicode

Total characters1576847
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS
ValueCountFrequency (%)
US778511
99.0%
MEX3143
 
0.4%
CAN2426
 
0.3%
PR1559
 
0.2%
724
 
0.1%
2021-03-23T20:34:02.239177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T20:34:02.320311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
us778511
99.1%
mex3143
 
0.4%
can2426
 
0.3%
pr1559
 
0.2%

Most occurring characters

ValueCountFrequency (%)
U778511
49.4%
S778511
49.4%
M3143
 
0.2%
E3143
 
0.2%
X3143
 
0.2%
C2426
 
0.2%
A2426
 
0.2%
N2426
 
0.2%
P1559
 
0.1%
R1559
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1576847
100.0%

Most frequent character per category

ValueCountFrequency (%)
U778511
49.4%
S778511
49.4%
M3143
 
0.2%
E3143
 
0.2%
X3143
 
0.2%
C2426
 
0.2%
A2426
 
0.2%
N2426
 
0.2%
P1559
 
0.1%
R1559
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1576847
100.0%

Most frequent character per script

ValueCountFrequency (%)
U778511
49.4%
S778511
49.4%
M3143
 
0.2%
E3143
 
0.2%
X3143
 
0.2%
C2426
 
0.2%
A2426
 
0.2%
N2426
 
0.2%
P1559
 
0.1%
R1559
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1576847
100.0%

Most frequent character per block

ValueCountFrequency (%)
U778511
49.4%
S778511
49.4%
M3143
 
0.2%
E3143
 
0.2%
X3143
 
0.2%
C2426
 
0.2%
A2426
 
0.2%
N2426
 
0.2%
P1559
 
0.1%
R1559
 
0.1%

posEntryMode
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
05
315035 
09
236481 
02
195934 
90
 
19576
80
 
15283

Length

Max length2
Median length2
Mean length1.98968924
Min length0

Characters and Unicode

Total characters1564618
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row02
2nd row09
3rd row09
4th row09
5th row02
ValueCountFrequency (%)
05315035
40.1%
09236481
30.1%
02195934
24.9%
9019576
 
2.5%
8015283
 
1.9%
4054
 
0.5%
2021-03-23T20:34:02.531210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T20:34:02.613953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
05315035
40.3%
09236481
30.2%
02195934
25.0%
9019576
 
2.5%
8015283
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0782309
50.0%
5315035
20.1%
9256057
 
16.4%
2195934
 
12.5%
815283
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1564618
100.0%

Most frequent character per category

ValueCountFrequency (%)
0782309
50.0%
5315035
20.1%
9256057
 
16.4%
2195934
 
12.5%
815283
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common1564618
100.0%

Most frequent character per script

ValueCountFrequency (%)
0782309
50.0%
5315035
20.1%
9256057
 
16.4%
2195934
 
12.5%
815283
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1564618
100.0%

Most frequent character per block

ValueCountFrequency (%)
0782309
50.0%
5315035
20.1%
9256057
 
16.4%
2195934
 
12.5%
815283
 
1.0%

posConditionCode
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
01
628787 
08
149634 
99
 
7533
 
409

Length

Max length2
Median length2
Mean length1.998959768
Min length0

Characters and Unicode

Total characters1571908
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01
2nd row01
3rd row01
4th row01
5th row01
ValueCountFrequency (%)
01628787
80.0%
08149634
 
19.0%
997533
 
1.0%
409
 
0.1%
2021-03-23T20:34:02.832714image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T20:34:02.913691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
01628787
80.0%
08149634
 
19.0%
997533
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0778421
49.5%
1628787
40.0%
8149634
 
9.5%
915066
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1571908
100.0%

Most frequent character per category

ValueCountFrequency (%)
0778421
49.5%
1628787
40.0%
8149634
 
9.5%
915066
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common1571908
100.0%

Most frequent character per script

ValueCountFrequency (%)
0778421
49.5%
1628787
40.0%
8149634
 
9.5%
915066
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1571908
100.0%

Most frequent character per block

ValueCountFrequency (%)
0778421
49.5%
1628787
40.0%
8149634
 
9.5%
915066
 
1.0%
Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
online_retail
202156 
fastfood
112138 
entertainment
80098 
food
75490 
online_gifts
66238 
Other values (14)
250243 

Length

Max length20
Median length12
Mean length9.93804388
Min length3

Characters and Unicode

Total characters7814910
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrideshare
2nd rowentertainment
3rd rowmobileapps
4th rowmobileapps
5th rowfastfood
ValueCountFrequency (%)
online_retail202156
25.7%
fastfood112138
14.3%
entertainment80098
 
10.2%
food75490
 
9.6%
online_gifts66238
 
8.4%
rideshare51136
 
6.5%
hotels34097
 
4.3%
fuel23910
 
3.0%
subscriptions22901
 
2.9%
auto21651
 
2.8%
Other values (9)96548
12.3%
2021-03-23T20:34:03.136530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
online_retail202156
25.1%
fastfood112138
13.9%
entertainment80098
 
9.9%
food75490
 
9.4%
online_gifts66238
 
8.2%
rideshare51136
 
6.3%
hotels34097
 
4.2%
fuel23910
 
3.0%
subscriptions22901
 
2.8%
auto21651
 
2.7%
Other values (10)115512
14.3%

Most occurring characters

ValueCountFrequency (%)
e991808
12.7%
n876374
11.2%
i806271
10.3%
o791769
10.1%
t737066
9.4%
l615464
7.9%
a555983
7.1%
r492698
6.3%
f403346
 
5.2%
s399467
 
5.1%
Other values (13)1144664
14.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7509103
96.1%
Connector Punctuation285461
 
3.7%
Space Separator18964
 
0.2%
Other Punctuation1382
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e991808
13.2%
n876374
11.7%
i806271
10.7%
o791769
10.5%
t737066
9.8%
l615464
8.2%
a555983
7.4%
r492698
6.6%
f403346
5.4%
s399467
5.3%
Other values (10)838857
11.2%
ValueCountFrequency (%)
_285461
100.0%
ValueCountFrequency (%)
18964
100.0%
ValueCountFrequency (%)
/1382
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7509103
96.1%
Common305807
 
3.9%

Most frequent character per script

ValueCountFrequency (%)
e991808
13.2%
n876374
11.7%
i806271
10.7%
o791769
10.5%
t737066
9.8%
l615464
8.2%
a555983
7.4%
r492698
6.6%
f403346
5.4%
s399467
5.3%
Other values (10)838857
11.2%
ValueCountFrequency (%)
_285461
93.3%
18964
 
6.2%
/1382
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII7814910
100.0%

Most frequent character per block

ValueCountFrequency (%)
e991808
12.7%
n876374
11.2%
i806271
10.3%
o791769
10.1%
t737066
9.4%
l615464
7.9%
a555983
7.1%
r492698
6.3%
f403346
 
5.2%
s399467
 
5.1%
Other values (13)1144664
14.6%

currentExpDate
Categorical

HIGH CARDINALITY

Distinct165
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
03/2029
 
5103
08/2024
 
5087
10/2023
 
5075
05/2027
 
5063
01/2021
 
5041
Other values (160)
760994 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters5504541
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row06/2023
2nd row02/2024
3rd row08/2025
4th row08/2025
5th row10/2029
ValueCountFrequency (%)
03/20295103
 
0.6%
08/20245087
 
0.6%
10/20235075
 
0.6%
05/20275063
 
0.6%
01/20215041
 
0.6%
10/20325039
 
0.6%
08/20305029
 
0.6%
08/20225026
 
0.6%
07/20315022
 
0.6%
05/20265021
 
0.6%
Other values (155)735857
93.6%
2021-03-23T20:34:03.403025image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
03/20295103
 
0.6%
08/20245087
 
0.6%
10/20235075
 
0.6%
05/20275063
 
0.6%
01/20215041
 
0.6%
10/20325039
 
0.6%
08/20305029
 
0.6%
08/20225026
 
0.6%
07/20315022
 
0.6%
05/20265021
 
0.6%
Other values (155)735857
93.6%

Most occurring characters

ValueCountFrequency (%)
21605016
29.2%
01558893
28.3%
/786363
14.3%
1441079
 
8.0%
3368935
 
6.7%
7125901
 
2.3%
5125315
 
2.3%
6124091
 
2.3%
8123490
 
2.2%
4123283
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4718178
85.7%
Other Punctuation786363
 
14.3%

Most frequent character per category

ValueCountFrequency (%)
21605016
34.0%
01558893
33.0%
1441079
 
9.3%
3368935
 
7.8%
7125901
 
2.7%
5125315
 
2.7%
6124091
 
2.6%
8123490
 
2.6%
4123283
 
2.6%
9122175
 
2.6%
ValueCountFrequency (%)
/786363
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5504541
100.0%

Most frequent character per script

ValueCountFrequency (%)
21605016
29.2%
01558893
28.3%
/786363
14.3%
1441079
 
8.0%
3368935
 
6.7%
7125901
 
2.3%
5125315
 
2.3%
6124091
 
2.3%
8123490
 
2.2%
4123283
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII5504541
100.0%

Most frequent character per block

ValueCountFrequency (%)
21605016
29.2%
01558893
28.3%
/786363
14.3%
1441079
 
8.0%
3368935
 
6.7%
7125901
 
2.3%
5125315
 
2.3%
6124091
 
2.3%
8123490
 
2.2%
4123283
 
2.2%

accountOpenDate
Categorical

HIGH CARDINALITY

Distinct1820
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2014-06-21
 
33623
2014-09-30
 
13335
2014-05-22
 
11353
2012-10-09
 
10867
2014-10-02
 
10653
Other values (1815)
706532 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters7863630
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row2015-03-14
2nd row2015-03-14
3rd row2015-03-14
4th row2015-03-14
5th row2015-08-06
ValueCountFrequency (%)
2014-06-2133623
 
4.3%
2014-09-3013335
 
1.7%
2014-05-2211353
 
1.4%
2012-10-0910867
 
1.4%
2014-10-0210653
 
1.4%
2014-11-056693
 
0.9%
2014-12-046584
 
0.8%
2015-03-015962
 
0.8%
2015-02-215881
 
0.7%
2015-08-145503
 
0.7%
Other values (1810)675909
86.0%
2021-03-23T20:34:03.675735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2014-06-2133623
 
4.3%
2014-09-3013335
 
1.7%
2014-05-2211353
 
1.4%
2012-10-0910867
 
1.4%
2014-10-0210653
 
1.4%
2014-11-056693
 
0.9%
2014-12-046584
 
0.8%
2015-03-015962
 
0.8%
2015-02-215881
 
0.7%
2015-08-145503
 
0.7%
Other values (1810)675909
86.0%

Most occurring characters

ValueCountFrequency (%)
01791694
22.8%
-1572726
20.0%
11491726
19.0%
21339947
17.0%
5425469
 
5.4%
4381282
 
4.8%
3264152
 
3.4%
9178943
 
2.3%
6158049
 
2.0%
7131384
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6290904
80.0%
Dash Punctuation1572726
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
01791694
28.5%
11491726
23.7%
21339947
21.3%
5425469
 
6.8%
4381282
 
6.1%
3264152
 
4.2%
9178943
 
2.8%
6158049
 
2.5%
7131384
 
2.1%
8128258
 
2.0%
ValueCountFrequency (%)
-1572726
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7863630
100.0%

Most frequent character per script

ValueCountFrequency (%)
01791694
22.8%
-1572726
20.0%
11491726
19.0%
21339947
17.0%
5425469
 
5.4%
4381282
 
4.8%
3264152
 
3.4%
9178943
 
2.3%
6158049
 
2.0%
7131384
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII7863630
100.0%

Most frequent character per block

ValueCountFrequency (%)
01791694
22.8%
-1572726
20.0%
11491726
19.0%
21339947
17.0%
5425469
 
5.4%
4381282
 
4.8%
3264152
 
3.4%
9178943
 
2.3%
6158049
 
2.0%
7131384
 
1.7%

dateOfLastAddressChange
Categorical

HIGH CARDINALITY

Distinct2184
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2016-03-15
 
3819
2016-01-06
 
3740
2016-01-04
 
3558
2016-06-08
 
3355
2016-04-04
 
3194
Other values (2179)
768697 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters7863630
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st row2015-03-14
2nd row2015-03-14
3rd row2015-03-14
4th row2015-03-14
5th row2015-08-06
ValueCountFrequency (%)
2016-03-153819
 
0.5%
2016-01-063740
 
0.5%
2016-01-043558
 
0.5%
2016-06-083355
 
0.4%
2016-04-043194
 
0.4%
2016-02-223102
 
0.4%
2016-03-233091
 
0.4%
2016-04-073036
 
0.4%
2016-05-192969
 
0.4%
2016-01-012925
 
0.4%
Other values (2174)753574
95.8%
2021-03-23T20:34:03.969814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2016-03-153819
 
0.5%
2016-01-063740
 
0.5%
2016-01-043558
 
0.5%
2016-06-083355
 
0.4%
2016-04-043194
 
0.4%
2016-02-223102
 
0.4%
2016-03-233091
 
0.4%
2016-04-073036
 
0.4%
2016-05-192969
 
0.4%
2016-01-012925
 
0.4%
Other values (2174)753574
95.8%

Most occurring characters

ValueCountFrequency (%)
01795546
22.8%
-1572726
20.0%
11449902
18.4%
21258981
16.0%
6557064
 
7.1%
5299459
 
3.8%
4243269
 
3.1%
3235552
 
3.0%
9157186
 
2.0%
7147127
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6290904
80.0%
Dash Punctuation1572726
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
01795546
28.5%
11449902
23.0%
21258981
20.0%
6557064
 
8.9%
5299459
 
4.8%
4243269
 
3.9%
3235552
 
3.7%
9157186
 
2.5%
7147127
 
2.3%
8146818
 
2.3%
ValueCountFrequency (%)
-1572726
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7863630
100.0%

Most frequent character per script

ValueCountFrequency (%)
01795546
22.8%
-1572726
20.0%
11449902
18.4%
21258981
16.0%
6557064
 
7.1%
5299459
 
3.8%
4243269
 
3.1%
3235552
 
3.0%
9157186
 
2.0%
7147127
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII7863630
100.0%

Most frequent character per block

ValueCountFrequency (%)
01795546
22.8%
-1572726
20.0%
11449902
18.4%
21258981
16.0%
6557064
 
7.1%
5299459
 
3.8%
4243269
 
3.1%
3235552
 
3.0%
9157186
 
2.0%
7147127
 
1.9%

cardCVV
Real number (ℝ≥0)

HIGH CORRELATION

Distinct899
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean544.4673376
Minimum100
Maximum998
Zeros0
Zeros (%)0.0%
Memory size6.0 MiB
2021-03-23T20:34:04.094691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile141
Q1310
median535
Q3785
95-th percentile948
Maximum998
Range898
Interquartile range (IQR)475

Descriptive statistics

Standard deviation261.5242203
Coefficient of variation (CV)0.4803304114
Kurtosis-1.248157188
Mean544.4673376
Median Absolute Deviation (MAD)236
Skewness0.046386637
Sum428148969
Variance68394.91778
MonotocityNot monotonic
2021-03-23T20:34:04.232812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86933749
 
4.3%
28915509
 
2.0%
64010804
 
1.4%
45510279
 
1.3%
9597024
 
0.9%
9176503
 
0.8%
5485487
 
0.7%
5865245
 
0.7%
2965125
 
0.7%
4945038
 
0.6%
Other values (889)681600
86.7%
ValueCountFrequency (%)
100192
 
< 0.1%
1011000
0.1%
102136
 
< 0.1%
103224
 
< 0.1%
1041303
0.2%
ValueCountFrequency (%)
998850
 
0.1%
997314
 
< 0.1%
9962294
0.3%
995961
0.1%
994118
 
< 0.1%

enteredCVV
Real number (ℝ≥0)

HIGH CORRELATION

Distinct976
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean544.1838566
Minimum0
Maximum998
Zeros8
Zeros (%)< 0.1%
Memory size6.0 MiB
2021-03-23T20:34:04.382230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile141
Q1310
median535
Q3785
95-th percentile948
Maximum998
Range998
Interquartile range (IQR)475

Descriptive statistics

Standard deviation261.5512537
Coefficient of variation (CV)0.4806303063
Kurtosis-1.246938755
Mean544.1838566
Median Absolute Deviation (MAD)236
Skewness0.04621024635
Sum427926050
Variance68409.0583
MonotocityNot monotonic
2021-03-23T20:34:04.549062image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86933424
 
4.3%
28915401
 
2.0%
64010731
 
1.4%
45510176
 
1.3%
9596963
 
0.9%
9176440
 
0.8%
5485438
 
0.7%
5865202
 
0.7%
2965081
 
0.6%
4945005
 
0.6%
Other values (966)682502
86.8%
ValueCountFrequency (%)
08
< 0.1%
11
 
< 0.1%
36
< 0.1%
41
 
< 0.1%
52
 
< 0.1%
ValueCountFrequency (%)
998846
 
0.1%
997316
 
< 0.1%
9962262
0.3%
995950
0.1%
994119
 
< 0.1%

cardLast4Digits
Real number (ℝ≥0)

Distinct5245
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4757.417799
Minimum0
Maximum9998
Zeros5578
Zeros (%)0.7%
Memory size6.0 MiB
2021-03-23T20:34:04.687404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile482
Q12178
median4733
Q37338
95-th percentile9522
Maximum9998
Range9998
Interquartile range (IQR)5160

Descriptive statistics

Standard deviation2996.58381
Coefficient of variation (CV)0.6298761085
Kurtosis-1.277451616
Mean4757.417799
Median Absolute Deviation (MAD)2569
Skewness0.07904789115
Sum3741057333
Variance8979514.53
MonotocityNot monotonic
2021-03-23T20:34:04.820620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59332946
 
4.2%
219410867
 
1.4%
600210172
 
1.3%
65806747
 
0.9%
85026553
 
0.8%
21785930
 
0.8%
05578
 
0.7%
76295235
 
0.7%
27014826
 
0.6%
28644533
 
0.6%
Other values (5235)692976
88.1%
ValueCountFrequency (%)
05578
0.7%
123
 
< 0.1%
2495
 
0.1%
371
 
< 0.1%
411
 
< 0.1%
ValueCountFrequency (%)
999831
 
< 0.1%
999744
 
< 0.1%
9996111
< 0.1%
999462
< 0.1%
99939
 
< 0.1%

transactionType
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
PURCHASE
745193 
REVERSAL
 
20303
ADDRESS_VERIFICATION
 
20169
 
698

Length

Max length20
Median length8
Mean length8.300680475
Min length0

Characters and Unicode

Total characters6527348
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPURCHASE
2nd rowPURCHASE
3rd rowPURCHASE
4th rowPURCHASE
5th rowPURCHASE
ValueCountFrequency (%)
PURCHASE745193
94.8%
REVERSAL20303
 
2.6%
ADDRESS_VERIFICATION20169
 
2.6%
698
 
0.1%
2021-03-23T20:34:05.060904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T20:34:05.143801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
purchase745193
94.8%
reversal20303
 
2.6%
address_verification20169
 
2.6%

Most occurring characters

ValueCountFrequency (%)
R826137
12.7%
E826137
12.7%
A805834
12.3%
S805834
12.3%
C765362
11.7%
P745193
11.4%
U745193
11.4%
H745193
11.4%
I60507
 
0.9%
V40472
 
0.6%
Other values (7)161486
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter6507179
99.7%
Connector Punctuation20169
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
R826137
12.7%
E826137
12.7%
A805834
12.4%
S805834
12.4%
C765362
11.8%
P745193
11.5%
U745193
11.5%
H745193
11.5%
I60507
 
0.9%
V40472
 
0.6%
Other values (6)141317
 
2.2%
ValueCountFrequency (%)
_20169
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6507179
99.7%
Common20169
 
0.3%

Most frequent character per script

ValueCountFrequency (%)
R826137
12.7%
E826137
12.7%
A805834
12.4%
S805834
12.4%
C765362
11.8%
P745193
11.5%
U745193
11.5%
H745193
11.5%
I60507
 
0.9%
V40472
 
0.6%
Other values (6)141317
 
2.2%
ValueCountFrequency (%)
_20169
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII6527348
100.0%

Most frequent character per block

ValueCountFrequency (%)
R826137
12.7%
E826137
12.7%
A805834
12.3%
S805834
12.3%
C765362
11.7%
P745193
11.4%
U745193
11.4%
H745193
11.4%
I60507
 
0.9%
V40472
 
0.6%
Other values (7)161486
 
2.5%

echoBuffer
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
786363 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
786363
100.0%
2021-03-23T20:34:05.349936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T20:34:05.416852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

currentBalance
Real number (ℝ≥0)

ZEROS

Distinct487318
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4508.739089
Minimum0
Maximum47498.81
Zeros33678
Zeros (%)4.3%
Memory size6.0 MiB
2021-03-23T20:34:05.683690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.74
Q1689.91
median2451.76
Q35291.095
95-th percentile14600.382
Maximum47498.81
Range47498.81
Interquartile range (IQR)4601.185

Descriptive statistics

Standard deviation6457.442068
Coefficient of variation (CV)1.432205754
Kurtosis14.61307813
Mean4508.739089
Median Absolute Deviation (MAD)2010.45
Skewness3.362136708
Sum3545505596
Variance41698558.06
MonotocityNot monotonic
2021-03-23T20:34:05.811542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033678
 
4.3%
8.4626
 
< 0.1%
5.6719
 
< 0.1%
29.4219
 
< 0.1%
59.6818
 
< 0.1%
18.8617
 
< 0.1%
10.0417
 
< 0.1%
116.6917
 
< 0.1%
117.2816
 
< 0.1%
3.5816
 
< 0.1%
Other values (487308)752520
95.7%
ValueCountFrequency (%)
033678
4.3%
0.012
 
< 0.1%
0.025
 
< 0.1%
0.034
 
< 0.1%
0.044
 
< 0.1%
ValueCountFrequency (%)
47498.811
< 0.1%
47496.841
< 0.1%
47496.321
< 0.1%
47493.831
< 0.1%
47493.741
< 0.1%

merchantCity
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
786363 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
786363
100.0%
2021-03-23T20:34:06.103386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T20:34:06.169901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

merchantState
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
786363 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
786363
100.0%
2021-03-23T20:34:06.331208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T20:34:06.400870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

merchantZip
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
786363 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
786363
100.0%
2021-03-23T20:34:06.567837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T20:34:06.635934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size768.1 KiB
False
433495 
True
352868 
ValueCountFrequency (%)
False433495
55.1%
True352868
44.9%
2021-03-23T20:34:06.667362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

posOnPremises
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
786363 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
786363
100.0%
2021-03-23T20:34:06.843657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T20:34:06.951246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

recurringAuthInd
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
786363 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
786363
100.0%
2021-03-23T20:34:07.196416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T20:34:07.276175image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size768.1 KiB
False
785320 
True
 
1043
ValueCountFrequency (%)
False785320
99.9%
True1043
 
0.1%
2021-03-23T20:34:07.312232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

isFraud
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size768.1 KiB
False
773946 
True
 
12417
ValueCountFrequency (%)
False773946
98.4%
True12417
 
1.6%
2021-03-23T20:34:07.360326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Interactions

2021-03-23T20:33:16.714841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:17.120318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:17.426235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:17.742557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:18.080622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:18.371279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:18.655170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:18.931873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:19.201846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:19.506596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:19.769112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:20.041813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:20.528449image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:20.799581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:21.066664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:21.382505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:21.641670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:21.910799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:22.176384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:22.459077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:22.764128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:23.026206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:23.289506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:23.540403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:23.775138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:24.026142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:24.278404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:24.562777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:24.821856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:25.064932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:25.314727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:25.570488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:25.813780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:26.086019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:26.345824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:26.627052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:26.885887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:27.134707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:27.387770image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:27.662798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:27.921070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:28.162640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:28.407147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:28.647928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:28.897819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:29.157633image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:29.401883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:29.644885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:29.883675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:30.138941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:30.391881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:30.637708image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:30.888746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:31.144861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:31.389111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:31.813349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:32.044003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:32.284475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:32.522786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:32.761399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:33.015826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:33.266177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:33.524287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:33.760602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:33.990683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:34.214981image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:34.436755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:34.669965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:34.920405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:35.154630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:35.375629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T20:33:35.592357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-03-23T20:34:07.616964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-23T20:34:07.942618image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-23T20:34:08.194121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-23T20:34:08.508189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-03-23T20:33:40.758006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-23T20:33:45.067551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

accountNumbercustomerIdcreditLimitavailableMoneytransactionDateTimetransactionAmountmerchantNameacqCountrymerchantCountryCodeposEntryModeposConditionCodemerchantCategoryCodecurrentExpDateaccountOpenDatedateOfLastAddressChangecardCVVenteredCVVcardLast4DigitstransactionTypeechoBuffercurrentBalancemerchantCitymerchantStatemerchantZipcardPresentposOnPremisesrecurringAuthIndexpirationDateKeyInMatchisFraud
073726505673726505650005000.002016-08-13T14:27:3298.55UberUSUS0201rideshare06/20232015-03-142015-03-144144141803PURCHASE0.00FalseFalseFalse
173726505673726505650005000.002016-10-11T05:05:5474.51AMC #191138USUS0901entertainment02/20242015-03-142015-03-14486486767PURCHASE0.00TrueFalseFalse
273726505673726505650005000.002016-11-08T09:18:397.47Play StoreUSUS0901mobileapps08/20252015-03-142015-03-14486486767PURCHASE0.00FalseFalseFalse
373726505673726505650005000.002016-12-10T02:14:507.47Play StoreUSUS0901mobileapps08/20252015-03-142015-03-14486486767PURCHASE0.00FalseFalseFalse
483032909183032909150005000.002016-03-24T21:04:4671.18Tim Hortons #947751USUS0201fastfood10/20292015-08-062015-08-068858853143PURCHASE0.00TrueFalseFalse
583032909183032909150005000.002016-04-19T16:24:2730.76In-N-Out #422833USUS0201fastfood01/20202015-08-062015-08-068858853143PURCHASE0.00TrueFalseFalse
683032909183032909150005000.002016-05-21T14:50:3557.28Krispy Kreme #685312USUS0201fastfood05/20202015-08-062015-08-068858853143PURCHASE0.00TrueFalseFalse
783032909183032909150005000.002016-06-03T00:31:219.37Shake Shack #968081USUS0501fastfood01/20212015-08-062015-08-068858853143PURCHASE0.00TrueFalseFalse
883032909183032909150004990.632016-06-10T01:21:46523.67Burger King #486122US0201fastfood08/20322015-08-062015-08-068858853143PURCHASE9.37TrueFalseFalse
983032909183032909150005000.002016-07-11T10:47:16164.37Five Guys #510989USUS0508fastfood04/20202015-08-062015-08-068858853143PURCHASE0.00TrueFalseFalse

Last rows

accountNumbercustomerIdcreditLimitavailableMoneytransactionDateTimetransactionAmountmerchantNameacqCountrymerchantCountryCodeposEntryModeposConditionCodemerchantCategoryCodecurrentExpDateaccountOpenDatedateOfLastAddressChangecardCVVenteredCVVcardLast4DigitstransactionTypeechoBuffercurrentBalancemerchantCitymerchantStatemerchantZipcardPresentposOnPremisesrecurringAuthIndexpirationDateKeyInMatchisFraud
7863537328525057328525055000049796.872016-12-10T12:24:38190.41EZ Putt Putt #804489USUS0901entertainment06/20292012-08-232012-08-239369363783PURCHASE203.13TrueFalseFalse
7863547328525057328525055000049606.462016-12-10T23:29:1183.97KFC #206511USUS0508fastfood12/20302012-08-232012-08-239399393388PURCHASE393.54TrueFalseFalse
7863557328525057328525055000049522.492016-12-18T14:06:34157.22UberUSUS0901rideshare08/20292012-08-232012-08-239369363783PURCHASE477.51FalseFalseFalse
7863567328525057328525055000049365.272016-12-19T03:25:53408.83LyftUSUS0901rideshare06/20212012-08-232012-08-239399393388PURCHASE634.73FalseFalseFalse
7863577328525057328525055000048956.442016-12-22T07:27:2351.48Sunoco Gas #380975USUS0508fuel07/20322012-08-232012-08-239369363783PURCHASE1043.56TrueFalseFalse
7863587328525057328525055000048904.962016-12-22T18:44:12119.92LyftUSUS9001rideshare12/20222012-08-232012-08-239369363783PURCHASE1095.04FalseFalseFalse
7863597328525057328525055000048785.042016-12-25T16:20:3418.89hulu.comUSUS0901online_subscriptions08/20232012-08-232012-08-239399393388PURCHASE1214.96FalseFalseFalse
7863607328525057328525055000048766.152016-12-27T15:46:2449.43LyftUSUS0201rideshare08/20252012-08-232012-08-239369363783PURCHASE1233.85FalseFalseFalse
7863617328525057328525055000048716.722016-12-29T00:30:5549.89walmart.comUSUS0999online_retail07/20222012-08-232012-08-239399393388PURCHASE1283.28FalseFalseFalse
7863627328525057328525055000048666.832016-12-30T20:10:2972.18UberUSUS0501rideshare05/20242012-08-232012-08-239399393388PURCHASE1333.17FalseFalseFalse